The goal of this study (specific aim one) is to demonstrate the application of methods for the identification of characteristics in stabilized schizophrenia patients that can be used to guide clinicians in the choice of maintenance treatment. In this project we combine the use of existing data from a double-blind randomized control longitudinal study of maintenance treatment in schizophrenia, a four-component medical typology for disaggregating heterogeneity of treatment response, and multivariate statistics for identifying treatment moderators and latent subgroup trajectories. This approach addresses the long-standing needs to reduce heterogeneity in schizophrenia through the identification of treatment-response subgroups, and to develop evidence-based adaptations of general treatment guidelines for select subgroups of patients. Data is drawn from the NIMH funded, multi-site Treatment Strategies in Schizophrenia (TSS) study by Schooler et al. comparing maintenance medication treatments for stabilized DSM-IIIR schizophrenia and schizoaffective subjects (N=313) assigned to: (a) standard dose medication, (b) low-dose medication (20% of standard dose), or (c) targeted intervention (medication only when symptomatic) with regular evaluations over a 2-year follow- up. Specific aim 1a addresses the clinical uncertainty regarding the best treatment for stabilized first-episode patients, testing the hypotheses that these patients have an equal or lower risk of relapse with low-dose or targeted prodromal treatments compared to standard dose medications. We then systematically investigate a central gap in schizophrenia knowledge, discriminating whether outcome predictors function as non-specific predictors or as treatment moderators. Our approach uses both effect-sizes and p-values to distinguish the role of predictors for each outcome. In addition to modeling predictors of relapse and time to relapse, in this submission we also estimate the role of predictors on several additional dependent variables (social functioning, psychopathology, negative and extrapyramidal symptoms (EPS)). In specific aim 1b, we employ a growth-mixture statistical approach to identifying latent trajectory classes of patient outcomes over time, estimate the effects of predictors on class membership, and evaluate whether different latent classes have differential responsiveness to treatment. As a whole, these analyses inform first-episode treatment guidelines, contribute to knowledge of nonspecific predictors in estimating risks without treatment, evaluate predictors of differential treatment efficacy that may point to mechanisms of treatment action, estimate the joint effects of predictors resulting in preliminary treatment response subgroup algorithms, and explore the exciting prospect of empirically identifying latent subgroup trajectories, their predictors, and differential responsiveness to treatment. If successful, we intend to generalize these methods to additional schizophrenia studies at acute and maintenance phases of the illness to systematically develop treatment-response subgroup knowledge to reduce the scientific problem of heterogeneity and improve outcomes for schizophrenia patients and society. The devastating effects of schizophrenia rank it within the top 10 causes of lifetime disability worldwide. Annual costs for treatment and lost productivity exceed $62 billion U.S. dollars per year and represent nearly 3% of the entire disease burden in the developing world. This research project seeks to both improve treatment outcomes and reduce costs by matching subgroups of patients to the most effective treatments. [unreadable] [unreadable] [unreadable]